Yueyue Fan

A two-stage stochastic programming model for transportation network protection

Network protection against natural and human-caused hazards has become a topical research theme in engineering and social sciences. This paper focuses on the problem of allocating limited retrofit resources over multiple highway bridges to improve the resilience and robustness of the entire transportation system in question. The main modeling challenges in network retrofit problems are to capture the interdependencies among individual transportation facilities and to cope with the extremely high uncertainty in the decision environment. In this paper, we model the network retrofit problem as a two-stage stochastic programming problem that optimizes a mean-risk objective of the system loss. This formulation hedges well against uncertainty, but also imposes computational challenges due to involvement of integer decision variables and increased dimension of the problem. An efficient algorithm is developed, via extending the well-known L-shaped method using generalized benders decomposition, to efficiently handle the binary integer variables in the first stage and the nonlinear recourse in the second stage of the model formulation. The proposed modeling and solution methods are general and can be applied to other network design problems as well.

Seismic Risk Assessment of Transportation Network Systems

When evaluating the earthquake risk to transportation system it is important to take into account the integrated effect of ground motion, liquefaction, and landslides on the network components and system. In this article, the risk from earthquakes to a transportation system is evaluated in terms of direct loss from damage to bridges and travel delays in the transportation network. The contribution of site effects to the loss from damage to bridges is estimated using the San Francisco Bay area as a test bed. Damage and loss to bridges from ground shaking and ground displacements (vertical and horizontal) from liquefaction and landslides are computed for a magnitude 7.0 scenario earthquake on the Hayward fault in California. It is found that liquefaction damage is the largest contributor to the repair cost which is used as a measure of the loss from damage. The performance of the transportation network is evaluated in terms of travel delay times. Travel delays resulting from damage due to ground shaking and changes in travel times are evaluated for the scenario event under the assumptions of fixed and variable travel demands. It is found that with fixed travel demand, the post-event travel times increase significantly. Travel times remain relatively unchanged and decrease with the variable demand time assumption.

Arriving-on-Time Problem: Discrete Algorithm That Ensures Convergence

Finding optimal paths in stochastic networks is an important topic in many scientific and engineering fields. To cope with uncertainty, various performance measures, including expected travel time, reliability, value at risk, and expectation with chance constraint, have been introduced. The literature has shown that adaptive strategies that incorporate both anticipation and real-time information are more efficient than a preplanned strategy in a stochastic environment. Most adaptive optimal-path algorithms focus on least-expected travel time. Recently, an adaptive path-finding strategy, named the stochastic on-time arrival (SOTA) algorithm, was proposed to maximize the travel time reliability for any given time threshold. However, the originally proposed SOTA algorithm is based on the classic successive approximation. Whether this algorithm converges within a finite number of successive approximation steps is an open question. In this study, the proposed discrete SOTA algorithm runs in an optimal polynomial time and thus improves the computational efficiency significantly. Numerical examples are provided, and future extensions are proposed.

Optimal Allocation of Multiple Emergency Service Resources for Protection of Critical Transportation Infrastructure

Optimal deployment of limited emergency resources in a large area is of interest to public agencies at all levels. In this paper, the problem of allocating limited emergency service vehicles including fire engines, fire trucks, and ambulances among a set of candidate stations is formulated as a mixed integer linear programming model, in which the objective is to maximize the service coverage of critical transportation infrastructure (CTI). On the basis of this model, the effects of demand at CTI nodes and of transportation network performance on the optimal coverage of CTI are studied. In addition, given a fixed total budget, the most efficient distribution of investment among the three types of emergency service vehicles is identified. To cope with the uncertainty involved in some of the model parameters such as traffic network performance, formulations based on various risk preferences are proposed. The concept of regret is applied to evaluate the robustness of proposed resource allocation strategies. The applicability of the proposed methodologies to high-density metropolitan areas is demonstrated through a case study that uses data from current practice in Singapore.

An Integrated Biofuel Supply Chain to Cope with Feedstock Seasonality and Uncertainty

A biofuel supply chain consists of various interdependent components from feedstock resources all the way to energy demand sites. This study focuses on the design of an efficient biofuel supply chain system against seasonal variations and uncertainties of feedstock supply in an integrative manner. By integrating planning and operational decisions in a stochastic programming framework, we aim at finding an effective design strategy for biofuel supply chain that is economically viable and hedges well against a wide range of future uncertainties. A solution algorithm based on scenario decomposition is designed to overcome computational challenges involved in large-scale applications. A California case study is implemented to demonstrate the applicability of the proposed methods in evaluating the economic potential, the infrastructure needs, and the risk of waste-based bioethanol production.

Modeling Uncertainties in Emergency Service Resource Allocation

The problem of allocating multiple emergency service resources to protect critical transportation infrastructures is studied in this paper. Different modeling approaches, including deterministic, stochastic programming, and robust optimization, are used to model various risk preferences in decision making under uncertain service availability and accessibility. Singapore is used as a case study for numerical experiments. The performances of different models are compared in terms of allocation strategies and the reliability and robustness of the system.

Highway Network Retrofit under Seismic Hazard

In this paper, we use a two-stage stochastic programming model to optimize retrofit decision for highway systems so that damage caused by future earthquakes will be minimized. This work is an example of applying network theories and system optimization approaches to address important problems in the field of critical infrastructure protection. Using a real-world case study based on existing Alameda County highway network settings, we demonstrate potential real-world applications of this research. Comparison between the proposed approach and some commonly used methods in practice indicates significant potential benefit from more rigorous system approaches. Other issues such as value of perfect information, reliability, and robustness are also discussed.

The role of biomass in California's hydrogen economy

This paper presents the results of a model of hydrogen production from waste biomass in California. We develop a profit-maximizing model of a biomass hydrogen industry from field to vehicle tank. This model is used to estimate the economic potential for hydrogen production from two waste biomass resources in Northern California—wheat straw and rice straw—taking into account the on the ground geographic dimensions of both biomass supply and hydrogen demand. The systems analysis approach allows for explicit consideration of the interactions between feedstock collection, hydrogen production, and hydrogen distribution in finding the optimal system design. This case study approach provides insight into both the real-world potential and the real-world cost of producing hydrogen from waste biomass. Additional context is provided through the estimation of Californias total waste biomass hydrogen potential. We find that enough biomass is available from waste sources to provide up to 40% of the current California passenger car fuel demand as hydrogen. Optimized supply chains result in delivered hydrogen costing between

Dynamic programming and pseudo-inverses

3/kg and

Modeling Transportation Network Flows as a Simultaneous Function of Travel Demand, Earthquake Damage, and Network Level Service

5.50/kg with one-tenth of the well-to-wheels greenhouse gas emissions of conventional gasoline-fueled vehicles.